Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.path import Path
import matplotlib.patches as patches
%matplotlib inline
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
images_with_chessboard_corners = []
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
images_with_chessboard_corners.append(img)
# Horizontal layout
# https://stackoverflow.com/questions/19471814/display-multiple-images-in-one-ipython-notebook-cell
plt.figure(figsize=(50,30))
columns = 4
for i, image in enumerate(images_with_chessboard_corners):
plt.subplot(len(images_with_chessboard_corners) / columns + 1, columns, i + 1)
plt.imshow(image)
Then I will compute camera calibration matrix and distortion coefficients¶
In [2]:
def cal_undistort(img, objpoints, imgpoints):
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#ret, corners = cv2.findChessboardCorners(gray, (8,6), None)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
def plot_two_images(img1, img2, modified_image_text="Undistorted Image"):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
ax2.set_title(modified_image_text, fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Let's display a few of the chessboard images, but undistorted¶
In [3]:
# Display a few images
img = cv2.imread('camera_cal/calibration19.jpg')
plot_two_images(img, cal_undistort(img, objpoints, imgpoints))
img = cv2.imread('camera_cal/calibration1.jpg')
plot_two_images(img, cal_undistort(img, objpoints, imgpoints))
img = cv2.imread('camera_cal/calibration10.jpg')
plot_two_images(img, cal_undistort(img, objpoints, imgpoints))
Looks good! Now let's apply the same undistortion to some of the test images¶
In [4]:
img = cv2.imread('test_images/test1.jpg')
plot_two_images(img, cal_undistort(img, objpoints, imgpoints))
img = cv2.imread('test_images/test2.jpg')
plot_two_images(img, cal_undistort(img, objpoints, imgpoints))
img = cv2.imread('test_images/test3.jpg')
plot_two_images(img, cal_undistort(img, objpoints, imgpoints))
In [5]:
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh_min=0, thresh_max=255):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 2) Take the derivative in x or y given orient = 'x' or 'y'
if (orient == 'x'):
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
abs_sobel = np.absolute(sobelx)
if (orient == 'y'):
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
abs_sobel = np.absolute(sobely)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# 6) Return this mask as your binary_output image
#binary_output = np.copy(img) # Remove this line
return binary_output
def plot_threshold_gradient(img1, img2, label="Abs Sobel Threshold"):
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(img2, cmap='gray')
ax2.set_title(label, fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
image = cv2.imread('test_images/test1.jpg')
grad_binary = abs_sobel_thresh(image, orient='x', thresh_min=20, thresh_max=110)
plot_threshold_gradient(image, grad_binary)
image = cv2.imread('test_images/test2.jpg')
grad_binary = abs_sobel_thresh(image, orient='x', thresh_min=20, thresh_max=110)
plot_threshold_gradient(image, grad_binary)
image = cv2.imread('test_images/test3.jpg')
grad_binary = abs_sobel_thresh(image, orient='x', thresh_min=20, thresh_max=110)
plot_threshold_gradient(image, grad_binary)
Magnitude Threshold¶
In [6]:
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Calculate the magnitude
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
gradmag = np.sqrt(sobelx**2 + sobely**2)
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
return binary_output
image = cv2.imread('test_images/test3.jpg')
mag_binary = mag_thresh(image, sobel_kernel=3, mag_thresh=(30, 100))
plot_threshold_gradient(image, mag_binary, "Magnitude Gradient")
Direction Threshold¶
In [7]:
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
return binary_output
image = cv2.imread('test_images/test6.jpg')
dir_binary = dir_threshold(image, sobel_kernel=15, thresh=(0.7, 1.3))
plot_threshold_gradient(image, dir_binary, "Direction Gradient")
Color Threshold¶
Initially I didn't use any color gradient and it was hard for the algorithm to detect the lanes in the area where there were tire marks or shadows, so we need to explore the HLS space to pick out the lane better
In [43]:
def hls_threshold(image, thresh_l=(145,255), thres_s=(190,255)):
hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
H = hls[:,:,0]
L = hls[:,:,1]
S = hls[:,:,2]
s_binary = np.zeros_like(S)
s_binary[(S > thres_s[0]) & (S <= thres_s[1])] = 1
l_binary = np.zeros_like(L)
l_binary[(L > thresh_l[0]) & (L <= thresh_l[1])] = 1
return l_binary, s_binary
image = cv2.imread('test_images/test5.jpg')
l_binary, s_binary = hls_threshold(image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(s_binary, cmap = 'gray')
ax2.set_title('S-channel', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
Combined gradient¶
Now that we have 3 different types of gradients, let's combine them'
In [45]:
image = cv2.imread('test_images/test2.jpg')
#image = mpimg.imread('test_images/test3.jpg')
# Choose a Sobel kernel size
def combined_gradient(image):
ksize = 3 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh_min=20, thresh_max=110)
grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh_min=20, thresh_max=110)
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=(30, 100))
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=(0, np.pi/2))
l_binary, s_binary = hls_threshold(image)
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1)) | (s_binary == 1)] = 1
return combined
plot_threshold_gradient(image, combined_gradient(image), "Combined Gradient")
Perspective transform¶
Let us now create a perspective transform. We will use the straight lines images from the test directory in order to get the necessary trapezoid coordinates.
In [46]:
image = cv2.imread('test_images/straight_lines1.jpg')
#image = mpimg.imread('test_images/straight_lines1.jpg')
# 258, 50 -> lower left
# 603, 273 -> upper left
# 681, 273 -> upper right
# 1041, 50 -> lower right
# I used this section to select the points
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.plot(258, 670, ".")
plt.plot(590, 450, ".")
plt.plot(690, 450, ".")
plt.plot(1041, 670, ".")
Out[46]:
In [47]:
src = np.float32([[258, 670],
[590, 450],
[690, 450],
[1041, 670]])
dst = np.float32([[258, 720],
[258, 0],
[1041, 0],
[1041, 720]])
# Define perspective transform funtion
def perspective_transform(img):
img_size = (img.shape[1], img.shape[0])
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
return warped
warped_im = perspective_transform(image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title = "Source Image"
ax1.imshow(image)
poly = plt.Polygon(src, closed=True, fill=False, color='#FF0000')
ax1.add_patch(poly)
ax2.set_title = "Warped Image"
ax2.imshow(warped_im)
poly = plt.Polygon(dst, closed=True, fill=False, color='#FF0000')
ax2.add_patch(poly)
Out[47]:
Let's see how this looks like on a a curved lane¶
In [48]:
image = mpimg.imread('test_images/test2.jpg')
warped_im = perspective_transform(image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title = "Source Image"
ax1.imshow(image)
poly = plt.Polygon(src, closed=True, fill=False, color='#FF0000')
ax1.add_patch(poly)
ax2.set_title = "Warped Image"
ax2.imshow(warped_im)
poly = plt.Polygon(dst, closed=True, fill=False, color='#FF0000')
ax2.add_patch(poly)
Out[48]:
Now let's apply this transform on an image with threshold¶
In [49]:
#image = cv2.imread('test_images/test6.jpg')
#image = mpimg.imread('test_images/test3.jpg')
# Choose a Sobel kernel size
ksize = 3 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh_min=20, thresh_max=110)
grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh_min=20, thresh_max=110)
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=(30, 100))
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=(0, np.pi/2))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
binary_warped = perspective_transform(combined)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title = "Source Image"
ax1.imshow(image)
poly = plt.Polygon(src, closed=True, fill=False, color='#FF0000')
ax1.add_patch(poly)
ax2.set_title = "Warped Image"
ax2.imshow(binary_warped, cmap='gray')
poly = plt.Polygon(dst, closed=True, fill=False, color='#FF0000')
ax2.add_patch(poly)
Out[49]:
Now let us find the lanes!¶
We will be using a histogram to determine the intensity of the white pixels
In [50]:
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
plt.plot(histogram)
Out[50]:
In [51]:
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),
(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),
(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
In [52]:
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[52]:
In [53]:
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
In [54]:
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[54]:
In [55]:
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
Minv = cv2.getPerspectiveTransform(dst, src)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(image, 1, newwarp, 0.3, 0)
plt.imshow(result)
Out[55]:
Now let's create a pipeline and feed it multiple images¶
In [56]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
In [57]:
def radius_and_offset(left_fit, right_fit, warped_combination):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Fit new polynomials to left (x,y) and right (x,y) pixel points in world space
# Does it make more sense to test curvature on previous fits or the raw points?
#left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
#right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
y_points = np.linspace(0, warped_combination.shape[0]-1, warped_combination.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
left_fit_cr = np.polyfit(y_points*ym_per_pix, left_fitx*xm_per_pix, 2)
right_fit_cr = np.polyfit(y_points*ym_per_pix, right_fitx*xm_per_pix, 2)
# Evaluation point
#y_eval = np.max(lefty) # bottom of the image
y_eval = np.max(y_points) # bottom of the image
# Calculate the left and right curvatures
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Average curvature of the lane
average_curverad = (left_curverad+right_curverad)/2
'''
Offset in lane
'''
bottom_y = image.shape[0] #image shape = [720,1280]
left_fit_bottom = left_fit[0]*bottom_y**2 + left_fit[1]*bottom_y + left_fit[2]
right_fit_bottom = right_fit[0]*bottom_y**2 + right_fit[1]*bottom_y + right_fit[2]
lane_center = (left_fit_bottom + right_fit_bottom)/2.
offset_pix = image.shape[1]/2 - lane_center # in pixels, image shape = [720,1280]
offset_m = offset_pix*xm_per_pix
return left_curverad, right_curverad, average_curverad, offset_m
In [58]:
# The final pipeline
def process_image(img):
combined_gradient_binary = combined_gradient(img)
binary_warped = perspective_transform(combined_gradient_binary)
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),
(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),
(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(img, 1, newwarp, 0.3, 0)
# Get the radius of curvature and offset
left_rad, right_rad, average_rad, offset_m = radius_and_offset(left_fit, right_fit, combined_gradient_binary)
average_rad_string = "Radius of Curvature: %.2f m" % average_rad
offset_string = "Center Offset: %.2f m" % offset_m
cv2.putText(result,average_rad_string , (110, 110), cv2.FONT_HERSHEY_PLAIN, 4, (255,255,255), thickness=2)
cv2.putText(result, offset_string, (110, 170), cv2.FONT_HERSHEY_PLAIN, 4, (255,255,255), thickness=2)
return result
# Make a list of test images
images = glob.glob('test_images/*.jpg')
images_with_lane_marking = []
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
images_with_lane_marking.append(process_image(img))
In [59]:
plt.figure(figsize=(50,30))
columns = 4
for i, image in enumerate(images_with_lane_marking):
plt.subplot(len(images_with_lane_marking) / columns + 1, columns, i + 1)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
Now let's record the video!¶
In [61]:
white_output = 'test_videos_output/project_video_result.mp4'
#clip1 = VideoFileClip("project_video.mp4").subclip(20,25)
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
Feeling adventurous, so let's try the challenge video based on the original pipeline¶
In [62]:
white_output = 'test_videos_output/project_video_challenge.mp4'
#clip1 = VideoFileClip("project_video.mp4").subclip(20,25)
clip1 = VideoFileClip("challenge_video.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)